{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_part_of_speech_ANC_example.ipynb","provenance":[{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599354735581}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"Qolj9DDIuG1Y"},"source":["![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/tutorial_docs/examples/colab/component_examples/part_of_speechPOS/NLU_part_of_speech_ANC_example.ipynb)\n","\n","\n","# Part of Speech tagging with NLU \n","\n","## Install Java and NLU"]},{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["Part of speech tags assign each token one of the following grammatical labels \n","  \n","\n","\n","\n","|Tag |Description | Example|\n","|------|------------|------|\n","|CC| Coordinating conjunction | This batch of mushroom stew is savory **and** delicious    |\n","|CD| Cardinal number | Here are **five** coins    |\n","|DT| Determiner | **The** bunny went home    |\n","|EX| Existential there | **There** is a storm coming    |\n","|FW| Foreign word | I'm having a **déjà vu**    |\n","|IN| Preposition or subordinating conjunction | He is cleverer **than** I am   |\n","|JJ| Adjective | She wore a **beautiful** dress    |\n","|JJR| Adjective, comparative | My house is **bigger** than yours    |\n","|JJS| Adjective, superlative | I am the **shortest** person in my family   |\n","|LS| List item marker | A number of things need to be considered before starting a business **,** such as premises **,** finance **,** product demand **,** staffing and access to customers |\n","|MD| Modal | You **must** stop when the traffic lights turn red    |\n","|NN| Noun, singular or mass | The **dog** likes to run    |\n","|NNS| Noun, plural | The **cars** are fast    |\n","|NNP| Proper noun, singular | I ordered the chair from **Amazon**  |\n","|NNPS| Proper noun, plural | We visted the **Kennedys**   |\n","|PDT| Predeterminer | **Both** the children had a toy   |\n","|POS| Possessive ending | I built the dog'**s** house    |\n","|PRP| Personal pronoun | **You** need to stop    |\n","|PRP$| Possessive pronoun | Remember not to judge a book by **its** cover |\n","|RB| Adverb | The dog barks **loudly**    |\n","|RBR| Adverb, comparative | Could you sing more **quietly** please?   |\n","|RBS| Adverb, superlative | Everyone in the race ran fast, but John ran **the fastest** of all    |\n","|RP| Particle | He ate **up** all his dinner    |\n","|SYM| Symbol | What are you doing **?**    |\n","|TO| to | Please send it back **to** me    |\n","|UH| Interjection | **Wow!** You look gorgeous    |\n","|VB| Verb, base form | We **play** soccer |\n","|VBD| Verb, past tense | I **worked** at a restaurant    |\n","|VBG| Verb, gerund or present participle | **Smoking** kills people   |\n","|VBN| Verb, past participle | She has **done** her homework    |\n","|VBP| Verb, non-3rd person singular present | You **flit** from place to place    |\n","|VBZ| Verb, 3rd person singular present | He never **calls** me    |\n","|WDT| Wh-determiner | The store honored the complaints, **which** were less than 25 days old    |\n","|WP| Wh-pronoun | **Who** can help me?    |\n","|WP\\$| Possessive wh-pronoun | **Whose** fault is it?    |\n","|WRB| Wh-adverb | **Where** are you going?  |\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619906803479,"user_tz":-120,"elapsed":117656,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6215753e-19b2-42e2-b91f-aa397b9242f0"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","  \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 22:04:46--  https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r-                     0%[                    ]       0  --.-KB/s               \r-                   100%[===================>]   1.63K  --.-KB/s    in 0s      \n","\n","2021-05-01 22:04:46 (34.4 MB/s) - written to stdout [1671/1671]\n","\n","Installing  NLU 3.0.0 with  PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K     |████████████████████████████████| 204.8MB 63kB/s \n","\u001b[K     |████████████████████████████████| 153kB 44.1MB/s \n","\u001b[K     |████████████████████████████████| 204kB 21.3MB/s \n","\u001b[K     |████████████████████████████████| 204kB 41.8MB/s \n","\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# NLU makes POS easy. \n","\n","You just need to load the POS model via ner.load() and predict on some dataset.    \n","It could be a pandas dataframe with a column named text or just an array of strings."]},{"cell_type":"code","metadata":{"id":"pmpZSNvGlyZQ","colab":{"base_uri":"https://localhost:8080/","height":394},"executionInfo":{"status":"ok","timestamp":1619906856795,"user_tz":-120,"elapsed":170962,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"69d43673-ce13-4304-fe8e-3b3dfa0fe361"},"source":["import nlu \n","\n","example_text =  [\"A person like Jim or Joe\", \n"," \"An organisation like Microsoft or PETA\",\n"," \"A location like Germany\",\n"," \"Anything else like Playstation\", \n"," \"Person consisting of multiple tokens like Angela Merkel or Donald Trump\",\n"," \"Organisations consisting of multiple tokens like JP Morgan\",\n"," \"Locations consiting of multiple tokens like Los Angeles\", \n"," \"Anything else made up of multiple tokens like Super Nintendo\",]\n","\n","nlu.load('pos').predict(example_text)[['pos','token']]"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>pos</th>\n","      <th>token</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[DT, NN, IN, NNP, CC, NNP]</td>\n","      <td>[A, person, like, Jim, or, Joe]</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>[DT, NN, IN, NNP, CC, NNP]</td>\n","      <td>[An, organisation, like, Microsoft, or, PETA]</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[DT, NN, IN, NNP]</td>\n","      <td>[A, location, like, Germany]</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>[NN, RB, IN, NNP]</td>\n","      <td>[Anything, else, like, Playstation]</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[NN, VBG, IN, JJ, NNS, IN, NNP, NNP, CC, NNP, ...</td>\n","      <td>[Person, consisting, of, multiple, tokens, lik...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>[NNP, VBG, IN, JJ, NNS, IN, NNP, NNP]</td>\n","      <td>[Organisations, consisting, of, multiple, toke...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>[NNP, VBG, IN, JJ, NNS, IN, NNP, NNP]</td>\n","      <td>[Locations, consiting, of, multiple, tokens, l...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>[NN, RB, VBN, RP, IN, JJ, NNS, IN, NNP, NNP]</td>\n","      <td>[Anything, else, made, up, of, multiple, token...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                 pos                                              token\n","0                         [DT, NN, IN, NNP, CC, NNP]                    [A, person, like, Jim, or, Joe]\n","1                         [DT, NN, IN, NNP, CC, NNP]      [An, organisation, like, Microsoft, or, PETA]\n","2                                  [DT, NN, IN, NNP]                       [A, location, like, Germany]\n","3                                  [NN, RB, IN, NNP]                [Anything, else, like, Playstation]\n","4  [NN, VBG, IN, JJ, NNS, IN, NNP, NNP, CC, NNP, ...  [Person, consisting, of, multiple, tokens, lik...\n","5              [NNP, VBG, IN, JJ, NNS, IN, NNP, NNP]  [Organisations, consisting, of, multiple, toke...\n","6              [NNP, VBG, IN, JJ, NNS, IN, NNP, NNP]  [Locations, consiting, of, multiple, tokens, l...\n","7       [NN, RB, VBN, RP, IN, JJ, NNS, IN, NNP, NNP]  [Anything, else, made, up, of, multiple, token..."]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"qgGdEUgkMika","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619907105989,"user_tz":-120,"elapsed":7981,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"40591daa-d68c-46fe-b626-6bf5588c0102"},"source":["text = [\"Barclays misled shareholders and the public about one of the biggest investments in the bank's history, a BBC Panorama investigation has found.\",\n","\"The bank announced in 2008 that Manchester City owner Sheikh Mansour had agreed to invest more than £3bn.\",\n","\"But the BBC found that the money, which helped Barclays avoid a bailout by British taxpayers, actually came from the Abu Dhabi government.\",\n","\"Barclays said the mistake in its accounts was 'a drafting error'.\",\n","\"Unlike RBS and Lloyds TSB, Barclays narrowly avoided having to request a government bailout late in 2008 after it was rescued by £7bn worth of new investment, most of which came from the Gulf states of Qatar and Abu Dhabi.\",\n","\"The S&P 500's price to earnings multiple is 71% higher than Apple's, and if Apple were simply valued at the same multiple, its share price would be $840, which is 52% higher than its current price.\",\n","\"Alice has a cat named Alice and also a dog named Alice and also a parrot named Alice, it is her favorite name!\"\n","] + example_text\n","pos_df = nlu.load('pos').predict(text, output_level='token')[['pos','token']]"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"STc7iOwtljGo"},"source":["## Lets explore our data which the predicted POS tags and visalize them!    \n","\n","We specify [1:] so we dont se the count for the O-tag wich is the most common, since most words in a sentence are not named entities and thus not part of a chunk"]},{"cell_type":"code","metadata":{"id":"UDSAYjadlfdK","colab":{"base_uri":"https://localhost:8080/","height":314},"executionInfo":{"status":"ok","timestamp":1619907198530,"user_tz":-120,"elapsed":795,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"3c48b8ac-646d-4498-8d81-f73d5fce1151"},"source":["\n","pos_df['pos'].value_counts()[1:].plot.bar(title='Occurence of Part of Speech tokens in dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f2704257b90>"]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"PC83y42kq8vd"},"source":["## We can merge the I-XXX and B-XXX tags for tokens with the same XXX tag for better insight   \n","\n","Let's define a dict to rename I-XXX and B-XXX to XXX.    \n","We can use the pandas [Dataframe.replace()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) function with a dict to add a column which has only the tag ORG, PER, LOC, MISC or O in it.\n","\n"]},{"cell_type":"code","metadata":{"id":"rlcEvP9tOSiy","colab":{"base_uri":"https://localhost:8080/","height":358},"executionInfo":{"status":"ok","timestamp":1619907202559,"user_tz":-120,"elapsed":958,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ff40b823-ffe5-417a-c495-c1e6a07801ab"},"source":["pos_type_to_viz = 'NNP'\n","pos_df[pos_df.pos == pos_type_to_viz]['token'].value_counts().plot.bar(title='Most often occuring NNP labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f270422c910>"]},"metadata":{"tags":[]},"execution_count":8},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"ks6NDXg7RXG3","colab":{"base_uri":"https://localhost:8080/","height":330},"executionInfo":{"status":"ok","timestamp":1619907205286,"user_tz":-120,"elapsed":819,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"fbb81ee3-3bcb-4bf8-f4e2-16b6c1b8ef4a"},"source":["pos_type_to_viz = 'JJ'\n","pos_df[pos_df.pos == pos_type_to_viz]['token'].value_counts().plot.bar(title='Most often occuring JJ labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f2702830a10>"]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"sQ8nKGB7qB29"},"source":["# NLU provides many more POS models!"]},{"cell_type":"code","metadata":{"id":"HmpMeRm_qElf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619907206187,"user_tz":-120,"elapsed":327,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"009edf7a-5140-48dd-b425-af2524a9328c"},"source":["nlu.print_all_model_kinds_for_action('pos')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language <nl> NLU provides the following Models : \n","nlu.load('nl.pos') returns Spark NLP model pos_ud_alpino\n","nlu.load('nl.pos.ud_alpino') returns Spark NLP model pos_ud_alpino\n","For language <en> NLU provides the following Models : \n","nlu.load('en.pos') returns Spark NLP model pos_anc\n","nlu.load('en.pos.anc') returns Spark NLP model pos_anc\n","nlu.load('en.pos.ud_ewt') returns Spark NLP model pos_ud_ewt\n","For language <fr> NLU provides the following Models : \n","nlu.load('fr.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('fr.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","For language <de> NLU provides the following Models : \n","nlu.load('de.pos.ud_hdt') returns Spark NLP model pos_ud_hdt\n","nlu.load('de.pos') returns Spark NLP model pos_ud_hdt\n","For language <it> NLU provides the following Models : \n","nlu.load('it.pos') returns Spark NLP model pos_ud_isdt\n","nlu.load('it.pos.ud_isdt') returns Spark NLP model pos_ud_isdt\n","For language <nb> NLU provides the following Models : \n","nlu.load('nb.pos.ud_bokmaal') returns Spark NLP model pos_ud_bokmaal\n","For language <nn> NLU provides the following Models : \n","nlu.load('nn.pos') returns Spark NLP model pos_ud_nynorsk\n","nlu.load('nn.pos.ud_nynorsk') returns Spark NLP model pos_ud_nynorsk\n","For language <pl> NLU provides the following Models : \n","nlu.load('pl.pos') returns Spark NLP model pos_ud_lfg\n","nlu.load('pl.pos.ud_lfg') returns Spark NLP model pos_ud_lfg\n","For language <pt> NLU provides the following Models : \n","nlu.load('pt.pos.ud_bosque') returns Spark NLP model pos_ud_bosque\n","nlu.load('pt.pos') returns Spark NLP model pos_ud_bosque\n","For language <ru> NLU provides the following Models : \n","nlu.load('ru.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","nlu.load('ru.pos') returns Spark NLP model pos_ud_gsd\n","For language <es> NLU provides the following Models : \n","nlu.load('es.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('es.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","For language <ar> NLU provides the following Models : \n","nlu.load('ar.pos') returns Spark NLP model pos_ud_padt\n","For language <hy> NLU provides the following Models : \n","nlu.load('hy.pos') returns Spark NLP model pos_ud_armtdp\n","For language <eu> NLU provides the following Models : \n","nlu.load('eu.pos') returns Spark NLP model pos_ud_bdt\n","For language <bn> NLU provides the following Models : \n","nlu.load('bn.pos') returns Spark NLP model pos_msri\n","For language <br> NLU provides the following Models : \n","nlu.load('br.pos') returns Spark NLP model pos_ud_keb\n","For language <bg> NLU provides the following Models : \n","nlu.load('bg.pos') returns Spark NLP model pos_ud_btb\n","nlu.load('bg.pos.ud_btb') returns Spark NLP model pos_ud_btb\n","For language <ca> NLU provides the following Models : \n","nlu.load('ca.pos') returns Spark NLP model pos_ud_ancora\n","For language <cs> NLU provides the following Models : \n","nlu.load('cs.pos') returns Spark NLP model pos_ud_pdt\n","nlu.load('cs.pos.ud_pdt') returns Spark NLP model pos_ud_pdt\n","For language <fi> NLU provides the following Models : \n","nlu.load('fi.pos.ud_tdt') returns Spark NLP model pos_ud_tdt\n","nlu.load('fi.pos') returns Spark NLP model pos_ud_tdt\n","For language <gl> NLU provides the following Models : \n","nlu.load('gl.pos') returns Spark NLP model pos_ud_treegal\n","For language <el> NLU provides the following Models : \n","nlu.load('el.pos') returns Spark NLP model pos_ud_gdt\n","nlu.load('el.pos.ud_gdt') returns Spark NLP model pos_ud_gdt\n","For language <he> NLU provides the following Models : \n","nlu.load('he.pos') returns Spark NLP model pos_ud_htb\n","nlu.load('he.pos.ud_htb') returns Spark NLP model pos_ud_htb\n","For language <hi> NLU provides the following Models : \n","nlu.load('hi.pos') returns Spark NLP model pos_ud_hdtb\n","For language <hu> NLU provides the following Models : \n","nlu.load('hu.pos') returns Spark NLP model pos_ud_szeged\n","nlu.load('hu.pos.ud_szeged') returns Spark NLP model pos_ud_szeged\n","For language <id> NLU provides the following Models : \n","nlu.load('id.pos') returns Spark NLP model pos_ud_gsd\n","For language <ga> NLU provides the following Models : \n","nlu.load('ga.pos') returns Spark NLP model pos_ud_idt\n","For language <da> NLU provides the following Models : \n","nlu.load('da.pos') returns Spark NLP model pos_ud_ddt\n","For language <ja> NLU provides the following Models : \n","nlu.load('ja.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('ja.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","For language <la> NLU provides the following Models : \n","nlu.load('la.pos') returns Spark NLP model pos_ud_llct\n","For language <lv> NLU provides the following Models : \n","nlu.load('lv.pos') returns Spark NLP model pos_ud_lvtb\n","For language <mr> NLU provides the following Models : \n","nlu.load('mr.pos') returns Spark NLP model pos_ud_ufal\n","For language <fa> NLU provides the following Models : \n","nlu.load('fa.pos') returns Spark NLP model pos_ud_perdt\n","For language <ro> NLU provides the following Models : \n","nlu.load('ro.pos') returns Spark NLP model pos_ud_rrt\n","nlu.load('ro.pos.ud_rrt') returns Spark NLP model pos_ud_rrt\n","For language <sk> NLU provides the following Models : \n","nlu.load('sk.pos') returns Spark NLP model pos_ud_snk\n","nlu.load('sk.pos.ud_snk') returns Spark NLP model pos_ud_snk\n","For language <sl> NLU provides the following Models : \n","nlu.load('sl.pos') returns Spark NLP model pos_ud_ssj\n","For language <sv> NLU provides the following Models : \n","nlu.load('sv.pos') returns Spark NLP model pos_ud_tal\n","nlu.load('sv.pos.ud_tal') returns Spark NLP model pos_ud_tal\n","For language <th> NLU provides the following Models : \n","nlu.load('th.pos') returns Spark NLP model pos_lst20\n","For language <tr> NLU provides the following Models : \n","nlu.load('tr.pos') returns Spark NLP model pos_ud_imst\n","nlu.load('tr.pos.ud_imst') returns Spark NLP model pos_ud_imst\n","For language <uk> NLU provides the following Models : \n","nlu.load('uk.pos') returns Spark NLP model pos_ud_iu\n","nlu.load('uk.pos.ud_iu') returns Spark NLP model pos_ud_iu\n","For language <yo> NLU provides the following Models : \n","nlu.load('yo.pos') returns Spark NLP model pos_ud_ytb\n","For language <zh> NLU provides the following Models : \n","nlu.load('zh.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('zh.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","nlu.load('zh.pos.ctb9') returns Spark NLP model pos_ctb9\n","nlu.load('zh.pos.ud_gsd_trad') returns Spark NLP model pos_ud_gsd_trad\n","For language <et> NLU provides the following Models : \n","nlu.load('et.pos') returns Spark NLP model pos_ud_edt\n","For language <ur> NLU provides the following Models : \n","nlu.load('ur.pos') returns Spark NLP model pos_ud_udtb\n","nlu.load('ur.pos.ud_udtb') returns Spark NLP model pos_ud_udtb\n","For language <ko> NLU provides the following Models : \n","nlu.load('ko.pos') returns Spark NLP model pos_ud_kaist\n","nlu.load('ko.pos.ud_kaist') returns Spark NLP model pos_ud_kaist\n","For language <bh> NLU provides the following Models : \n","nlu.load('bh.pos') returns Spark NLP model pos_ud_bhtb\n","For language <am> NLU provides the following Models : \n","nlu.load('am.pos') returns Spark NLP model pos_ud_att\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"weRI1oc4qGx2"},"source":[""],"execution_count":null,"outputs":[]}]}